Table 8. mycoSORT Results - Set of Features F1+F2+F3+F4.

Under-sampling(USF)	Classifier	Precision	Recall	F-measure	MCC	F-2
Training set with USF 0%	Naive Bayes	0.355	0.727	0.477	0.431	0.600
Training set with USF 0%	LMT	0.685	0.420	0.521	0.498	0.460
Training set with USF 0%	LibSVM	0.867	0.087	0.158	0.257	0.110
Training set with USF 5%	Naive Bayes	0.365	0.740	0.489	0.446	0.610
Training set with USF 5%	LMT	0.585	0.480	0.527	0.484	0.500
Training set with USF 5%	LibSVM	0.729	0.287	0.411	0.424	0.330
Training set with USF 10%	Naive Bayes	0.349	0.787	0.484	0.448	0.630
Training set with USF 10%	LMT	0.552	0.600	0.575	0.526	0.590
Training set with USF 10%	LibSVM	0.670	0.420	0.516	0.491	0.450
Training set with USF 15%	Naive Bayes	0.342	0.787	0.477	0.441	0.620
Training set with USF 15%	LMT	0.478	0.647	0.550	0.498	0.600
Training set with USF 15%	LibSVM	0.607	0.473	0.532	0.491	0.490
Training set with USF 20%	Naive Bayes	0.342	0.793	0.478	0.443	0.630
Training set with USF 20%	LMT	0.425	0.64	0.511	0.456	0.580
Training set with USF 20%	LibSVM	0.521	0.587	0.552	0.500	0.570
Training set with USF 25%	Naive Bayes	0.322	0.787	0.457	0.421	0.610
Training set with USF 25%	LMT	0.389	0.747	0.511	0.469	0.630
Training set with USF 25%	LibSVM	0.474	0.667	0.554	0.504	0.620
Training set with USF 30%	Naive Bayes	0.336	0.773	0.469	0.430	0.610
Training set with USF 30%	LMT	0.398	0.780	0.527	0.490	0.650
Training set with USF 30%	LibSVM	0.459	0.673	0.546	0.496	0.620
Training set with USF 35%	Naive Bayes	0.304	0.800	0.440	0.406	0.600
Training set with USF 35%	LMT	0.343	0.760	0.473	0.433	0.610
Training set with USF 35%	LibSVM	0.357	0.793	0.493	0.458	0.640
Training set with USF 40%	Naive Bayes	0.295	0.780	0.428	0.389	0.590
Training set with USF 40%	LMT	0.361	0.847	0.506	0.481	0.670
Training set with USF 40%	LibSVM	0.331	0.793	0.468	0.433	0.620

Results of Positive Class on Feature Setting #4, Using Bio-entities, Content and EC Numbers as Features.